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Nonlinear Additive ARX Models

 

作者: Rong Chen,   RueyS. Tsay,  

 

期刊: Journal of the American Statistical Association  （Taylor Available online 1993）
卷期: Volume 88, issue 423  

页码: 955-967

 

ISSN:0162-1459

 

年代: 1993

 

DOI:10.1080/01621459.1993.10476363

 

出版商: Taylor & Francis Group

 

关键词: Additivity;Alternating conditional expectation (ACE) algorithm;Best subset regression;BRUTO algorithm;River flow;Time series;Variable selection

 

数据来源: Taylor

 

摘要:

We consider in this article a class of nonlinear additive autoregressive models with exogenous variables for nonlinear time series analysis and propose two modeling procedures for building such models. The procedures proposed use two backfitting techniques (the ACE and BRUTO algorithms) to identify the nonlinear functions involved and use the methods of best subset regression and variable selection in regression analysis to determine the final model. Simulated and real examples are used to illustrate the analysis.

 

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